Mechanical performance monitoring of civil infrastructure using fiber Bragg grating (FBG) sensors has received significant public attention in recent years. However, there is currently little research on the mechanical performance monitoring of piles used in high-piled wharfs in coastal ports during pile driving using the FBG sensor technique. Based on the properties of precast prestressed concrete piles used in high-piled wharfs in coastal ports and servicing seawater environments, and the benefits of FBG sensors, the mechanical performance monitoring for precast prestressed concrete piles used in a newly-built high-piled wharf in the Tianjin Port of China is devised and deployed with the FBG sensor technique. To conduct performance monitoring of the precast prestressed concrete pile, a state-of-the-art FBG strain sensor, which is less thermosensitive and does not require temperature compensation, was used to monitor the strain status of different locations of the pile. In one pile, three of this kind of strain sensor were set near the head, middle and tip of the pile, and one FBG angle sensor was set near the head of the pile to measure the dip angle of the pile. During the testing, data were recorded for all of the details of the pile driving process. According to the data analysis, it is clear that the compressive strain at the middle of the pile during driving is larger than that near the head and tip of the pile. Therefore, the middle of the prestressed concrete pile is the key location that should be preferentially monitored during pile driving. Meanwhile, when the hammer impacts the pile continuously, the obvious tension strain at the tip of the pile increases and the maximum dynamic tension strain reaches 56 με, which approaches the tension ultimate strain. This occurs because the frictional resistance of soil is small in the middle of the pile when the tip meets the significant supporting soil layer. This study can provide a reference for the mechanical performance monitoring deployment of precast prestressed concrete piles used in high-piled wharf structures in coastal ports.

This paper aims to use deep learning algorithms to identify and study the stability of breakwater armor blocks. It introduces a posture identification model for fender blocks using a Mask Region-based Convolutional Neural Network (R-CNN), which has been enhanced by considering factors affecting breakwater fender blocks. Furthermore, a wave prediction model for breakwaters is developed by integrating Bidirectional Encoder Representations from Transformers (BERTs) with Bidirectional Long Short-Term Memory (BiLSTM). The performance of these models is evaluated. The results show that the accuracy of the Mask R-CNN and its comparison algorithms initially increases and then decreases with higher Intersection Over Union (IOU) thresholds, peaking at 95.16% accuracy at an IOU threshold of 0.5. The BERT-BiLSTM wave prediction model maintains a loss value around 0.01 and an accuracy of approximately 90.00%. These results suggest that the proposed models offer more accurate stability assessments of breakwater armor blocks. By combining the random forest prediction model with BiLSTM, the wave characteristics and fender posture can be predicted better, offering reliable decision support for breakwater engineering.

Through on-site research, research data analysis and engineering experience, the damage characteristics and damage types of high pile wharves in coastal ports were studied and analyzed, and their structural weaknesses and the first damage parts and components were analyzed. Then, a health monitoring system suitable for coastal high pile wharf structure was designed, and four main monitoring indexes, namely, displacement, strain (stress), ambient temperature and structural vibration, were determined through the analysis. Finally, the structural safety assessment method is determined based on each monitoring index of the coastal highpile jetty structural health monitoring system, and the assessment level based on the monitoring data is delineated, and the warning threshold of each monitoring index is determined, so as to lay a theoretical foundation for the analysis of the data and the early warning of the whole coastal highpile jetty structural health monitoring system.